Overview
Product details compiled from public sources, each with a citation.
- Vendor
- Snyk1
- Description
- Developer-security platform, now positioned as an AI security fabric, that secures AI-generated code and the AI agents and tools used to build and run AI-native applications.1
- Deployment
- SaaS3
- Status
- Active1
- Compliance
- SOC 2 Type II, ISO 27001, ISO 270174agent (company-level, see Methodology)
Matrix Coverage
Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.
| Asset class | Identify | Protect | Detect | Coverage | Source |
|---|---|---|---|---|---|
| AI Orchestration Tools | Protect: Not covered | Detect: Not covered | Secondary | 2 | |
| AI-Generated Code | Detect: Not covered | Primary | 5 | ||
| AI Agent Identities | Identify: Not covered | Secondary | 2 |
Framework Relevance
These frameworks include controls relevant to the asset classes Snyk defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Snyk implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | AI Orchestration Tools | Agents as deployed artifacts (orchestration view; see AI Agent Identities row for the principal view); system prompts and templates |
| AI Agent Identities | Agents as autonomous principals; Keys; Integrations and permissions | |
| CSA AI Controls Matrix | AI Orchestration Tools | Application and Interface Security; Supply Chain Management |
| AI-Generated Code | Application and Interface Security; Supply Chain Management | |
| AI Agent Identities | IAM; Governance, Risk and Compliance | |
| ISO 42001 | AI Orchestration Tools | A.6 AI system life cycle; A.5 Assessing impacts of AI systems |
| AI-Generated Code | A.6 AI system life cycle | |
| AI Agent Identities | A.9 Use of AI systems; A.3 Internal organization; A.5 Assessing impacts of AI systems | |
| Google SAIF | AI Orchestration Tools | Secure the AI supply chain; application and pipeline security; agent orchestration controls |
| AI-Generated Code | Secure the AI pipeline; code provenance and supply chain integrity | |
| AI Agent Identities | Focus on Agents (explicit SAIF section); identity, authorization, and delegation controls | |
| SANS Critical AI Security Guidelines | AI Orchestration Tools | Secure Agentic Systems and AI Autonomy Controls (defined function scope; execution isolation; API and function-call gating); Limit Model Behavior (focused functionality; access controls outside the model) |
| AI-Generated Code | Model I/O Handling (AI deployment in IDEs: prefer local-only integrations to limit exposure of code, keys, and proprietary data); Governance, Risk, Compliance (regularly test and red-team AI applications before and after deployment) | |
| AI Agent Identities | Secure Agentic Systems and AI Autonomy Controls (defined function scope; API and function-call gating; escalation and fallback); Limit Model Behavior (least-privilege focused functionality; human oversight; override capabilities) | |
| MITRE ATLAS | AI Orchestration Tools | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0016 Obtain Capabilities (malicious plugins) |
| AI-Generated Code | AML.T0010 AI Supply Chain Compromise (hallucinated dependencies and slopsquatting); AML.T0018 Manipulate AI Model (when models embed code-execution backdoors) | |
| AI Agent Identities | AML.T0053 AI Agent Tool Invocation; credential and delegation-chain abuse | |
| OWASP AI Exchange | AI Orchestration Tools | Development-time threats: agent framework supply chain; runtime threats: plugin abuse, prompt injection via tools |
| AI-Generated Code | Development-time threats: insecure code generation, license risk, hallucinated dependencies | |
| AI Agent Identities | Runtime threats: unauthorized agent actions, capability abuse, delegation chain exploitation | |
| OWASP LLM Top 10 | AI Orchestration Tools | LLM01 Prompt Injection; LLM05 Improper Output Handling; LLM07 System Prompt Leakage; LLM10 Unbounded Consumption |
| AI-Generated Code | LLM06 Excessive Agency (code execution); insecure or vulnerable code patterns inherited from training data | |
| AI Agent Identities | LLM06 Excessive Agency; LLM05 Improper Output Handling; unauthorized actions by AI agents | |
| OWASP Agentic Security Top 10 | AI Orchestration Tools | ASI01 Agent Goal Hijack; ASI02 Tool Misuse and Exploitation; ASI05 Unexpected Code Execution (RCE); ASI07 Insecure Inter-Agent Communication; ASI08 Cascading Failures; ASI10 Rogue Agents |
| AI-Generated Code | ASI05 Unexpected Code Execution (RCE); ASI04 Agentic Supply Chain Vulnerabilities (hallucinated dependencies and vibe-coding artifacts) | |
| AI Agent Identities | ASI03 Identity and Privilege Abuse; ASI10 Rogue Agents; ASI09 Human-Agent Trust Exploitation; ASI02 Tool Misuse and Exploitation (when tied to agent permissions) |
Provenance
Last sourced 2026-07-17.
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Sources
- Snyk AI Security Fabric homepage
- Snyk acquires Invariant Labs
- “The company's unique methods take into account contextual information, static scans of agent tools and implementations, runtime information, human annotations, and incident databases.”
- “For example, unauthorized data exfiltration to AI agents executing unintended actions, and threats like MCP vulnerabilities are already appearing in production.”
- Snyk deployment options
- Snyk Secure by Design
- “which includes both the controls set for ISO27001 and ISO27017.”
- Secure at Inception for AI
- “Snyk Studio integrates directly into AI assistants to proactively guide the AI to produce secure, high-quality code from the start, powered by Snyk Code and Snyk Open Source.”
Changelog
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Added verbatim source quotes to coverage and compliance citations.
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Enriched from Snyk primary sources; added coverage for MCP tooling and agent guardrails plus compliance attestations.
Found an error? Corrections are welcome. Suggest an edit.